1. Field of the Invention
The present invention relates to computerized simulation of hydrocarbon reservoirs in the earth, and in particular to reservoir surveillance of producing oil and gas fields to monitor and calibrate changes in the simulated fluid and rock properties of a reservoir.
2. Description of the Related Art
It has been common or conventional to simulate the fluid and rock properties of subsurface hydrocarbon reservoirs with computerized models. In recent years, a reservoir simulator with massive parallel processing capabilities for large scale reservoir simulation was developed by the assignee of the present application. The reservoir simulator was known as the POWERS simulator and was described in the literature. See, for example articles by Dogru, A. H., et al, “A Massively Parallel Reservoir Simulator for Large Scale Reservoir Simulation,” Paper SPE 51886 presented at the 1999 SPE Reservoir Simulation Symposium, Houston Tex., February 1999 and by Dogru, A. H., Dreiman, W. T., Hemanthkumar, K. and Fung, L. S., “Simulation of Super K Behavior in Ghawar by a Multi-Million Cell Parallel Simulator,” Paper SPE 68066 presented at the Middle East Oil Show, Bahrain, March 2001.
The analysis of multi-million-cell reservoir simulation results has been a relatively new challenge to the petroleum industry. Recently, as disclosed in commonly-owned U.S. patent application Ser. No. 10/916,851, “A HIGHLY-PARALLEL, IMPLICIT COMPOSITIONAL RESERVOIR SIMULATOR FOR MULTI-MILLION CELL MODELS,” filed Aug. 12, 2004, now U.S. Pat. No. 7,526,418, it has become possible to simulate giant datasets within practical time limits. With computer power making reservoir size and cell numbers less of a problem, the capability of human-machine interface to promptly interact and discern potential problem areas in the vast amounts of data has become a concern.
So far as is known, previous efforts have related either to advanced visualization of three-dimensional data from reservoir simulation or to data-mining approaches in attempts to achieve faster analysis.
Conventional visualization techniques have been generally sufficient when the simulation grid blocks have been on the order of some hundreds of thousands. A reservoir engineer's analysis time for datasets of this size has been comparable with computer processing turnaround time for simulation results. With multi-million-cell reservoir simulation, however, data analysis has become a significant bottleneck when conventional monitoring techniques have been used.
Reservoir surveillance of producing oil and gas fields has recently become of interest in the petroleum industry. The intent of reservoir surveillance has been to gather dynamic measurements which could potentially be used to improve management of a producing field, and to possibly optimize recovery of hydrocarbons. Dynamic measurements indicated changing conditions in the reservoir and were intended to provide a reservoir engineer with data complementary to the initial static or historical information from which reservoir simulation models were originally built. So far as is known, previous work in reservoir surveillance has related to development of equipment for performing field measurements and to design of surveys to gather data for surveillance.
Reservoir surveillance or monitoring has, so far as is known, been accomplished by acquiring real-time reservoir measurements to augment our knowledge about the reservoir. The fundamental premise in this data acquisition has been that dynamic measurements were indicative of substantive changes occurring in the reservoir. As fluids move during hydrocarbon production, by virtue of water displacing oil or by gas evolving as a gas cap that was previously dissolved in the oil, changes occur in the intrinsic properties of the reservoir, such as fluid density and sonic velocity.
Direct measurement of these changes is an indication of what is happening inside the reservoir. Present reservoir surveillance techniques include the following: (a) 4D or time-lapse seismic (repeated seismic surveying); (b) borehole gravimetry (direct density measurements at the borehole); (c) microseismic monitoring (sensing of micro-earthquakes occurring in the reservoir); and (d) electromagnetic resistivity monitoring (measuring electric resistance of reservoir fluids). As reservoir monitoring technologies have been applied in the last 10 years, it has become apparent that not all reservoirs respond equally well to these direct measurement techniques.
4D time-lapse seismic monitoring relies on the change in seismic amplitude (impedance and reflectivity) as fluids move inside the reservoir. Water displacing oil can have a dimming effect on the brightness of observed amplitudes. This has proven a useful monitoring technique in many fields. But in the case of giant reservoirs, such dimming may take many years to be observable with precision. Furthermore, this change can only be confidently established in areas with good seismic signal quality. Many reservoirs in the Middle East, for example, have a number of seismic data quality challenges that make 4D seismic of limited applicability and uncertain success.
Borehole gravimetry monitoring relies on observed changes of density at wellbore locations. Water displacing gas represents a very measurable density change. Water displacing oil represents a smaller but still measurable density change. In reservoirs with high salinity, however, these differences can be masked.
Microseismic monitoring relies on sensing micro-earthquakes generated by stress changes inside the reservoir. These stress changes occur because part of the reservoir rock, under a constant overburden stress, loses pore pressure due to fluid production escaping the rock. This increases the effective stress (which is the difference between overburden confining stress and pore pressure) and the subsequent rock deformation can produce cracks detectable by seismograms at wellbore stations. The consistency of the rock matrix is sometimes too brittle to crack with appreciable tremors, depending on the elastic properties of the rock.
Electromagnetic monitoring relies on measuring formation resistivity. Oil-bearing sands are highly resistive (i.e. low electrical conductivity), whereas water-bearing sands show low resistivity. Depending on the electric properties of the rock, one can relate resistivity change to oil saturation change.
So far as is known, conventional ways to refine or update an existing reservoir model has been by what is known as history matching using well production data from the reservoir. Other data such as that from reservoir surveillance techniques of the types mentioned was not included dynamically into adjustments of the reservoir model. As has been mentioned, time-lapse seismic simulations to indicate postulated changes in an existing model have been used, but seismic data does not directly relate to fluid or rock properties.